Tab. REF  presents the state-of-the-art comparison on the YouTube-VIS 2019 val. set. When using the ResNet-50 backbone, the recent one-stage PCAN [1]} and CrossVIS [2]} approaches achieve an overall mask accuracy (AP) of 36.1% and 36.3%, respectively. With the same ResNet-50 backbone, the first transformer-based VIS approach, VisTR [3]}, built on DETR framework achieves an overall mask AP of 35.6%. Among existing methods, the recently introduced SeqFormer [4]} based on Deformable DETR framework achieves the best overall accuracy with a mask AP of 47.4%. Our proposed MS-STS VIS approach outperforms SeqFormer [4]} by achieving an overall mask AP of 50.1%, using the same ResNet-50 backbone. Specifically, our MS-STS VIS provides an absolute gain of 4.8% at a higher overlap threshold of AP\(_{\mathtt {75}}\)  over SeqFormer. Similarly, our MS-STS VIS consistently outperforms SeqFormer with an overall mask AP of 51.1%, when using the ResNet-101 backbone. Finally, when using the recent Swin Transformer backbone, the proposed MS-STS VIS achieves the best accuracy reported in literature with an overall mask AP of 61.0%.
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